时,可以让卷积后的feature map的高和宽变大,就实现了分辨率的提升也就是超分辨重建,这个操作叫做sub-pixel convolution。 对于sub-pixel convolution,作者将一个H × W的低分辨率输入图像(Low Resolution)作为输入,低分辨率图像特征提取完毕后,生成n1个特征图,然后经过中间一堆操作等,不管有多少,只要到该上采
采用CNN对feature map 进行放大的方法,除了有 deconvolition 之外,还有一个叫做 sub- pixel convolution 。如果做SR(超分辨率)的话,需要将一张低分辨率图像转换成一张高分辨率图像。如果直接用 deconvolution 作为 upscale 手段的话,通常会带入过多人工因素进来。而 sub-pixel conv 会大大降低这个风险。先看 sub-pi...
transposed convolution是一个更好的名字,sub-pixel or fractional convolution可以看成是transposed convolution的一个特例。对一个常规的卷积层而言,前向传播时是convolution,将input feature map映射为output feature map,反向传播时则是transposed convolution,根据output feature map的梯度计算出input feature map的梯度,梯...
这样做使得算法的计算复杂度很高,ESPCN提出了一种在低分辨率场景下提取特征并重建图像的网络结构,降低了计算量,提高了运行...ESPCN的主要贡献,其后有很多方法都仿照此,在LR上提取特征,继而经过亚像素卷积层提高分辨率。(sub-pixelconvolution又称作pixelshuffle)一般通过卷积操作生成的特征...
| ESPCN (Efficient Sub-Pixel Convolutional Neural Network): 一种用于单帧图像超分辨率的深度学习模型。SRCNN (Super-Resolution Convolutional Neural Network): 这是一个经典的深度学习模型,用于图像超分辨率。VDSR (Very Deep Super-Resolution): VDSR 是一种非常深的卷积神经网络,用于单图像超分辨率。SRGAN (Super...
This repository contains an op-for-op PyTorch reimplementation of Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network. Table of contents ESPCN-PyTorch Overview Table of contents About Real-Time Single Image and Video Super-Resolution Using an ...
A PyTorch implementation of ESPCN based on CVPR 2016 paper "Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network" - leftthomas/ESPCN
As data used in this study is not required to have labels, we extract features by ResNet152, pretrained on ImageNet44 (see https://pytorch.org/vision/stable/models.html). The features are extracted by removing the last fully connected layer. To enable patch queries, an adaptive average ...
This block matrix is square if the number of channels remains unchanged after the convolutions. To reduce the number of channels, K needs to be a flat matrix; a tall block-matrix increases the number of channels. The collection of convolutional kernels at layer j is denoted by θj. Using...
A PyTorch implementation of ESPCN based on CVPR 2016 paper "Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network" - joker-xidian/ESPCN